This project acquires an advanced computing instrument at Tennessee Tech that brings together three complementary classes of accelerator technology, Accelerated Processing Units (APUs), Data Processing Units (DPUs), and Field-Programmable Gate Arrays (FPGAs), in a single, fully networked six-node cluster.
Combining these architectures in one machine gives researchers a rare opportunity to study how future computers can be designed and programmed to work faster and more efficiently together, and to apply that capability to real scientific and engineering problems. Planned research includes real-time inference of collaborative deep neural networks, hyperparameter optimization and neural architecture search, AI-driven blockchain security for the Internet of Things, parallel programming models spanning multiple forms of computational heterogeneity, smarter network computation, distributed quantum computing simulation, name-based compute and data placement, AI-driven networking and communications, and AI/ML-informed generation of fast collective algorithms.
The instrument will also support hands-on research training for undergraduate, master’s, and doctoral students, broadening participation in advanced computing and providing access to emerging accelerator hardware not typically available on public systems.
Funding: NSF Major Research Instrumentation (MRI) Track 1, Award #2511818 — Acquisition of a Multi-accelerator Architecture for Advanced Scalable Computing Research ($900,000, 2026-2029)
Team: Susmit Shannigrahi (Co-PI), Anthony Skjellum (PI), Syed Rafay Hasan (Co-PI), Muhammad Ismail (Co-PI), Amani Altarawneh (Co-PI), all at Tennessee Technological University.